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decision_forest_evaluator.hpp
/*
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#pragma once
#include <pcl/common/common.h>
#include <pcl/ml/dt/decision_forest.h>
#include <pcl/ml/dt/decision_forest_evaluator.h>
#include <pcl/ml/feature_handler.h>
#include <pcl/ml/stats_estimator.h>
#include <vector>
template <class FeatureType,
class DataSet,
class LabelType,
class ExampleIndex,
class NodeType>
pcl::DecisionForestEvaluator<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::
DecisionForestEvaluator()
: tree_evaluator_()
{}
template <class FeatureType,
class DataSet,
class LabelType,
class ExampleIndex,
class NodeType>
pcl::DecisionForestEvaluator<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::
~DecisionForestEvaluator() = default;
template <class FeatureType,
class DataSet,
class LabelType,
class ExampleIndex,
class NodeType>
void
pcl::DecisionForestEvaluator<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::
evaluate(pcl::DecisionForest<NodeType>& forest,
pcl::FeatureHandler<FeatureType, DataSet, ExampleIndex>& feature_handler,
pcl::StatsEstimator<LabelType, NodeType, DataSet, ExampleIndex>&
stats_estimator,
DataSet& data_set,
std::vector<ExampleIndex>& examples,
std::vector<LabelType>& label_data)
{
const std::size_t num_of_examples = examples.size();
label_data.resize(num_of_examples, 0);
for (std::size_t forest_index = 0; forest_index < forest.size(); ++forest_index) {
tree_evaluator_.evaluateAndAdd(forest[forest_index],
feature_handler,
stats_estimator,
data_set,
examples,
label_data);
}
const float inv_num_of_trees = 1.0f / static_cast<float>(forest.size());
for (std::size_t label_index = 0; label_index < label_data.size(); ++label_index) {
label_data[label_index] *= inv_num_of_trees;
}
}
template <class FeatureType,
class DataSet,
class LabelType,
class ExampleIndex,
class NodeType>
void
pcl::DecisionForestEvaluator<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::
evaluate(pcl::DecisionForest<NodeType>& forest,
pcl::FeatureHandler<FeatureType, DataSet, ExampleIndex>& feature_handler,
pcl::StatsEstimator<LabelType, NodeType, DataSet, ExampleIndex>&
stats_estimator,
DataSet& data_set,
ExampleIndex example,
std::vector<NodeType>& leaves)
{
leaves.resize(forest.size());
for (std::size_t forest_index = 0; forest_index < forest.size(); ++forest_index) {
NodeType leave;
tree_evaluator_.evaluate(forest[forest_index],
feature_handler,
stats_estimator,
data_set,
example,
leave);
leaves[forest_index] = leave;
}
}
pcl::DecisionForestEvaluator::~DecisionForestEvaluator
virtual ~DecisionForestEvaluator()
Destructor.
pcl::DecisionForestEvaluator::DecisionForestEvaluator
DecisionForestEvaluator()
Constructor.
Definition decision_forest_evaluator.hpp:54
pcl::DecisionForestEvaluator::evaluate
void evaluate(pcl::DecisionForest< NodeType > &DecisionForestEvaluator, pcl::FeatureHandler< FeatureType, DataSet, ExampleIndex > &feature_handler, pcl::StatsEstimator< LabelType, NodeType, DataSet, ExampleIndex > &stats_estimator, DataSet &data_set, std::vector< ExampleIndex > &examples, std::vector< LabelType > &label_data)
Evaluates the specified examples using the supplied forest.
Definition decision_forest_evaluator.hpp:73
pcl::DecisionForest
Class representing a decision forest.
Definition decision_forest.h:50
pcl::FeatureHandler
Utility class interface which is used for creating and evaluating features.
Definition feature_handler.h:49
pcl::StatsEstimator
Class interface for gathering statistics for decision tree learning.
Definition stats_estimator.h:49
common.h
Define standard C methods and C++ classes that are common to all methods.